Search decoded the vector blob into a []float32 and JSON-unmarshalled the
meta map for every row, then sorted all N and threw away everything past
topK. Meta only ever matters for a survivor, and the sort answered a
question a bounded heap answers cheaper.
The scan still visits every row — that is what picks the winners. What it
no longer does is allocate for a row it is about to discard. dotBlob reads
the vector out of its stored bytes, so scoring costs nothing; a row is
copied and its meta unmarshalled only once it has entered the topK.
At 10000 rows and topK 10: 70.6ms to 26.8ms, 58MB to 17.5MB, 240k allocs
to 60k.
Recall is unchanged where it is measured. recall+onnx scores 22/32 with
recall@1 70.4% and recall@3 85.2%, identical to before.
TestMemoryStoreSearchMatchesNaive pins the ranking against the full-sort
implementation it replaced, and TestDotBlobMatchesDot pins bit-identical
scores, which the 0.008 gate margin demands.
One behaviour did move: ties. sort.Slice is not stable, so equal scores
were ordered arbitrarily; the heap now keeps the earliest. Under the real
embedder an exact tie is a duplicate vector and nothing moved. Under the
hash embedder the eval's floor uses, everything ties at 0 and that run's
recall@3 went 74.1% to 81.5% — a number that measures tie order, not
retrieval. recall@1 and false recall, the two the eval asserts, are
unchanged on both runs.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01YMNNEkYx1mZFtHNrFk7uqb
claude
65ee0f9c61